Model-free and Vision-based Contact Force Control of Interventional Catheters
Bibliographic record
Abstract
Catheter-tissue contact force(CF) has been reported as one of the success factors of heart ablation surgery. Monitoring and controlling the CF are challenging processes due to the limitation of sensing technologies and robotic surgery systems. In the present study, an image-based and sensor-less CF controller system is proposed that benefits from a neural force feedback estimator. Firstly, a real-time and accurate force feedback element was designed and evaluated to estimate the CF directly from the image of the ablation catheter’s deflections during the surgery. This learning-based force estimator successfully could provide CF with a mean absolute error of 0.012 ± 0.01 N and it was implemented in a closed-loop controller as a feedback element. Then, an experimental setup including the ablation catheter manipulator mechanism and a camera was designed to evaluate the performance of the CF controller for static and dynamic input CFs. The evaluation tests showed that the system can maintain the CF with root-mean-squared(RMS) error of 0.01 ± 0.01 N for static test and RMS error of 0.04 ± 0.03 N for a dynamic input force.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".